Detect unusual points across numeric series with flexible rules. Review thresholds, compare methods, and export clear outlier reports easily.
| Index | Series Value | Expected Band | Comment |
|---|---|---|---|
| 1 | 12 | 12-17 | Normal baseline |
| 2 | 14 | 12-17 | Normal variation |
| 3 | 16 | 12-17 | Upper normal |
| 4 | 60 | 12-17 | Likely outlier |
Z-Score: z = (x - mean) / sample_stddev. A point is flagged when |z| > threshold.
Modified Z-Score: mz = 0.6745 * (x - median) / MAD. This is robust against extreme values and works well with skewed distributions.
IQR Method: Compute IQR = Q3 - Q1. Fences are Q1 - k*IQR and Q3 + k*IQR. Values outside fences are outliers.
Outlier detection improves reliability when teams analyze sensor logs, transaction streams, laboratory readings, and campaign metrics. This calculator supports three common methods, letting analysts compare sensitivity before removing records. In practice, organizations should investigate flagged points, not delete them immediately. A single extreme value can represent fraud, device failure, manual entry error, or an important event. Consistent review rules reduce reporting volatility, rework, stakeholder disputes, and audits during downstream analysis cycles consistently.
The IQR method works well for skewed distributions because quartiles resist distortion from extremes. Z-score is useful for roughly normal data and standardized monitoring thresholds. Modified z-score uses median and median absolute deviation, making it more stable for small samples or contaminated datasets. Analysts can run all three methods with the same series, compare flagged points, and document why a chosen threshold matched business tolerance, process risk, investigative capacity, and downstream reporting needs.
Thresholds control sensitivity and should align with operational consequences. A z-score threshold of 3.0 is a common starting point, while 2.5 increases detection volume. Modified z-score often starts near 3.5. For IQR, a 1.5 multiplier identifies typical anomalies, and 3.0 focuses on extreme values. Lower thresholds catch more suspicious points but raise review workload. Higher thresholds reduce false alarms yet may miss subtle process drift, gradual failure patterns, and recurring quality defects.
After submission, the calculator displays a summary above the form so reviewers can immediately see count, mean, standard deviation, and outlier totals. The detailed table then lists each index with z-score, modified z-score, fence limits, and final status. Teams can export CSV for audit trails, ticket attachments, or model governance records. PDF output supports meetings and approvals because the report format remains consistent across users, departments, dates, review sessions, and compliance evidence.
Professional use requires documented assumptions, threshold ownership, and traceable actions. Keep the original series, store cleaned versions separately, and note whether flagged points were corrected, excluded, or retained. Review thresholds periodically when seasonality, instrumentation, or customer behavior changes. For high-stakes decisions, pair statistical rules with domain checks, such as equipment maintenance logs or business calendars. This approach preserves signal quality while improving trust, reproducibility, and long-term analytical confidence for business decisions.
Start with IQR for skewed or unknown distributions. Use z-score when data is approximately normal. Use modified z-score for small samples or when extreme values may distort the mean and standard deviation.
No. It only flags suspicious points. You should review the context, confirm the cause, and then decide whether to correct, exclude, or keep each value.
Common defaults are z-score 3.0, modified z-score 3.5, and IQR multiplier 1.5. Adjust them based on risk, process stability, and the cost of false alarms.
Yes. The calculator accepts integers, decimals, and negative numbers. Separate values with commas, spaces, or line breaks in the series input box.
Each method uses different assumptions and statistics. Mean-based methods react strongly to extremes, while median and quartile methods are more robust in skewed or noisy datasets.
Use CSV for analysis, audits, and system imports. Use PDF when sharing a fixed report format for meetings, approvals, or compliance documentation.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.